Self-interest and support of climate-related transport policy measures: An empirical analysis for citizens in Germany and Sweden - Wolfgang Habla; Kumai Kokash; Åsa Löfgren; Anna Straubinger; Andreas Ziegler (ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung, 2024)
Based on data from broadly representative surveys among more than 1,400 citizens in Germany and Sweden, this paper empirically examines the support of different groups of climate-related (passenger) transport policy measures targeting vehicle use, public transport, air travel, and bicycle use. Our descriptive analysis reveals that pull policy measures (e.g. the financial support of public transport) are more strongly supported in both countries than push policy measures (e.g. the increase in taxes on flight tickets). Furthermore, bans (i.e. a sales ban on new gasoline- and diesel-powered vehicles and a ban on domestic flights) do not receive much support. Our econometric analysis with multivariate ordered and binary probit models points to the strong relevance of economic self-interest for the support of vehicle-, air travel-, and bicycle-related policy measures, i.e. citizens who are negatively affected by a certain measure are significantly more likely to disagree with it, while citizens who benefit from a certain measure are significantly more likely to support it. For example, owners of vehicles that run exclusively on conventional fuels are significantly less likely to agree with the introduction of road user charges on highways and especially the sales ban on new gasoline- and diesel-powered vehicles. Our econometric analysis also shows that environmental awareness and political identification play an important role for the agreement with most of the policy measures considered. Finally, we discuss our empirical results in the context of current policy debates in Germany and Sweden and some implications for policymakers.
Keywords: Q54; R48; L98; Q48; Q58; 330; Climate change; transport policy measures; individual support; multivariate probit models; simulated maximum likelihood estimation